public void Train(List <ClassifiableText> classifiableTexts)
{
// prepare input and ideal vectors
// input <- ClassifiableText text vector
// ideal <- characteristicValue vector
//
var input = GetInput(classifiableTexts);
var ideal = GetIdeal(classifiableTexts);
// train
//
Propagation train = new ResilientPropagation(_network, new BasicMLDataSet(input, ideal));
train.ThreadCount = 16;
NeuroNetworkEventArgs neroMessage;
// todo: throw exception if iteration count more than 1000
do
{
train.Iteration();
neroMessage = new NeuroNetworkEventArgs
{
Message =
$@"Training Classifier for {_characteristic.Name} characteristic. Errors:{train.Error * 100:0.00}%."
};
OnNeuroNetworkMessage(neroMessage);
} while (train.Error > 0.01);
train.FinishTraining();
neroMessage = new NeuroNetworkEventArgs
{
Message = $@"Classifier for {_characteristic.Name} characteristic trained. Wait..."
};
OnNeuroNetworkMessage(neroMessage);
}
protected virtual void OnNeuroNetworkMessage(NeuroNetworkEventArgs e)
{
NeuroNetworkMessage?.Invoke(this, e);
}
